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Average Current Mode (ACM) controlled synchronous DC–DC converters are increasingly required for computer power supplies and electric vehicle battery chargers. In this study, Generalized Hopfield Neural Network (GHNN) tuned PI controllers has been proposed for ACM control of synchronous single ended primary inductance converter (SEPIC). The dynamic converter model, required for designing the controllers, is derived using the state-space averaging technique considering all the converter non-idealities. In addition, modified Hankel matrix method is applied to reduce the derived converter model to its first-order equivalent to minimize the design complexity. Using the reduced-order model, the control loop errors are formulated as objective functions in terms of their PI controller parameters. These objective functions are then solved using GHNN to obtain the optimal PI controller parameters. The transient and steady-state performance of the converter with the proposed controller is studied and compared with controllers tuned using Zeigler Nicholas (ZN) method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Reduced-Order Linear Quadratic Regulator (ROLQR) control scheme for variations in the input voltage, reference voltage and load using MATLAB/ Simulink R2015b software tool. Results revealed that the proposed GHNN based controller has fast dynamic performance with less settling time, % overshoot, and steady-state error than existing control methods. A 100W converter prototype is also constructed and tested using TMS320C2000 TM  microcontroller to verify the effectiveness of the GHNN control algorithm.

Hopfield Neural Network-Based Average Current Mode Control of Synchronous SEPIC Converter